/usr/share/itpp/pyitpp.py is in libitpp-dev 4.3.1-8.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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# Brief: Loads an IT++ itfile content and outputs a dictionary with all variables found.
# Author: Bogdan Cristea
#
# Usage: from pyitpp import itload
# out = itload('fname.it')
#
# This module provides a function for loading itfile content into matrices/scalars
# and outputs all these variables as a dictionary whose keys are variable names as
# found in itfile. This module uses numpy module for matrix operations and numerical types.
# Thus, the provided functionality is similar to itload() function for MATLAB.
#
# -------------------------------------------------------------------------
#
# Copyright (C) 1995-2010 (see AUTHORS file for a list of contributors)
#
# This file is part of IT++ - a C++ library of mathematical, signal
# processing, speech processing, and communications classes and functions.
#
# IT++ is free software: you can redistribute it and/or modify it under the
# terms of the GNU General Public License as published by the Free Software
# Foundation, either version 3 of the License, or (at your option) any
# later version.
#
# IT++ is distributed in the hope that it will be useful, but WITHOUT ANY
# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
# details.
#
# You should have received a copy of the GNU General Public License along
# with IT++. If not, see <http://www.gnu.org/licenses/>.
#
# -------------------------------------------------------------------------
from sys import exit
from os import stat
from os import SEEK_SET
from stat import ST_SIZE
from struct import unpack
from numpy import mat
from numpy import reshape
from numpy.matlib import zeros
#numerical types used for conversions
from numpy import uint8
from numpy import int16
from numpy import int32
from numpy import float32
from numpy import float64
from numpy import complex64
from numpy import complex128
def __fgetstr(fid):
str = ''
while 1:
d = fid.read(1);
if d == '\x00':
break
str = str+d;
return str
def itload(in_file):
try:
f = open(in_file, 'rb')
except IOError:
try:
f = open(in_file+'.it', 'rb')
except IOError:
print 'Cannot open file'
exit()
#get file size
file_size = stat(in_file)[ST_SIZE]
#read IT++ magic string
magic = f.read(4);
if 'IT++' != magic:
print 'Not an IT++ file!'
exit()
#check the IT++ file version
version = f.read(1)
if 3 != ord(version):
print 'Only IT++ file version 3 is supported by this function!'
exit()
out = dict()#use a dictionary to output all tuples read from it file
while 1:
#save current file position
pos = f.tell()
#read header, data, and total block sizes (3*uint64)
header_data_block_sizes = unpack('3Q', f.read(24))
#read current variable name
var_name = __fgetstr(f)
#read current variable type
var_type = __fgetstr(f)
#skip header bytes
f.seek(pos+header_data_block_sizes[0], SEEK_SET)
if len(var_type) == 0: #A deleted entry -> skip it
pass
#scalars
# --- bin ---
elif 'bin' == var_type:
out[var_name] = uint8(unpack('b', f.read(1))[0])
# --- int8 (char) ---
elif 'int8' == var_type:
out[var_name] = unpack('c', f.read(1))[0]
# --- int16 (short) ---
elif 'int16' == var_type:
out[var_name] = int16(unpack('h', f.read(2))[0])
# --- int32 (int) ---
elif 'int32' == var_type:
out[var_name] = int32(unpack('i', f.read(4))[0])
# --- float32 (float) ---
elif 'float32' == var_type:
out[var_name] = float32(unpack('f', f.read(4))[0])
# --- float64 (double) ---
elif 'float64' == var_type:
out[var_name] = float64(unpack('d', f.read(8))[0])
# --- cfloat32 (complex<float>) ---
elif 'cfloat32' == var_type:
real_imag = unpack('2f', f.read(8))
out[var_name] = complex64(complex(real_imag[0], real_imag[1]))
# --- cfloat64 (complex<double>) ---
elif 'cfloat64' == var_type:
real_imag = unpack('2d', f.read(16))
out[var_name] = complex128(complex(real_imag[0], real_imag[1]))
#vectors
# --- bvec ---
elif 'bvec' == var_type:
length = unpack('Q', f.read(8))[0]
fmt = str(length)+'b'
out[var_name] = mat(unpack(fmt, f.read(length)), 'uint8').T#convert to a column vector
# --- string ---
elif 'string' == var_type:
length = unpack('Q', f.read(8))[0]
fmt = str(length)+'c'
out[var_name] = "".join(unpack(fmt, f.read(length)))
# --- svec ---
elif 'svec' == var_type:
length = unpack('Q', f.read(8))[0]
fmt = str(length)+'h'
out[var_name] = mat(unpack(fmt, f.read(length*2)), 'int16').T#convert to a column vector
# --- ivec ---
elif 'ivec' == var_type:
length = unpack('Q', f.read(8))[0]
fmt = str(length)+'i'
out[var_name] = mat(unpack(fmt, f.read(length*4)), 'int32').T#convert to a column vector
# --- fvec ---
elif 'fvec' == var_type:
length = unpack('Q', f.read(8))[0]
fmt = str(length)+'f'
out[var_name] = mat(unpack(fmt, f.read(length*4)), 'float32').T#convert to a column vector
# --- dvec ---
elif 'dvec' == var_type:
length = unpack('Q', f.read(8))[0]
fmt = str(length)+'d'
out[var_name] = mat(unpack(fmt, f.read(length*8)), 'float64').T#convert to a column vector
# --- fcvec ---
elif 'fcvec' == var_type:
length = unpack('Q', f.read(8))[0]
fmt = str(2*length)+'f'
real_imag = mat(unpack(fmt, f.read(2*length*4)), 'float32').T#convert to a column vector
out[var_name] = zeros((length, 1), complex)
for i in range(length):
out[var_name][i,0] = complex64(complex(real_imag[2*i], real_imag[2*i+1]))
# --- dcvec ---
elif 'dcvec' == var_type:
length = unpack('Q', f.read(8))[0]
fmt = str(2*length)+'d'
real_imag = mat(unpack(fmt, f.read(2*length*8)), 'float64').T#convert to a column vector
out[var_name] = zeros((length, 1), complex)
for i in range(length):
out[var_name][i,0] = complex128(complex(real_imag[2*i], real_imag[2*i+1]))
#matrices
# --- bmat ---
elif 'bmat' == var_type:
rows = unpack('Q', f.read(8))[0]
cols = unpack('Q', f.read(8))[0]
fmt = str(rows*cols)+'b'
out[var_name] = reshape(mat(unpack(fmt, f.read(rows*cols)), 'uint8'), (cols, rows)).T
# --- smat ---
elif 'smat' == var_type:
rows = unpack('Q', f.read(8))[0]
cols = unpack('Q', f.read(8))[0]
fmt = str(rows*cols)+'h'
out[var_name] = reshape(mat(unpack(fmt, f.read(rows*cols*2)), 'int16'), (cols, rows)).T
# --- imat ---
elif 'imat' == var_type:
rows = unpack('Q', f.read(8))[0]
cols = unpack('Q', f.read(8))[0]
fmt = str(rows*cols)+'i'
out[var_name] = reshape(mat(unpack(fmt, f.read(rows*cols*4)), 'int32'), (cols, rows)).T
# --- fmat ---
elif 'fmat' == var_type:
rows = unpack('Q', f.read(8))[0]
cols = unpack('Q', f.read(8))[0]
fmt = str(rows*cols)+'f'
out[var_name] = reshape(mat(unpack(fmt, f.read(rows*cols*4)), 'float32'), (cols, rows)).T
# --- dmat ---
elif 'dmat' == var_type:
rows = unpack('Q', f.read(8))[0]
cols = unpack('Q', f.read(8))[0]
fmt = str(rows*cols)+'d'
out[var_name] = reshape(mat(unpack(fmt, f.read(rows*cols*8)), 'float64'), (cols, rows)).T
# --- fcmat ---
elif 'fcmat' == var_type:
rows = unpack('Q', f.read(8))[0]
cols = unpack('Q', f.read(8))[0]
fmt = str(2*rows*cols)+'f'
real_imag = mat(unpack(fmt, f.read(2*rows*cols*4)), 'float32').T
out[var_name] = zeros((rows, cols), complex)
for i in range(rows):
for j in range(cols):
out[var_name] = out[var_name][i,j] = complex(real_imag[2*i+2*rows*j], real_imag[2*i+1+2*rows*j])
# --- dcmat ---
elif 'dcmat' == var_type:
rows = unpack('Q', f.read(8))[0]
cols = unpack('Q', f.read(8))[0]
fmt = str(2*rows*cols)+'d'
real_imag = mat(unpack(fmt, f.read(2*rows*cols*8)), 'float64').T
out[var_name] = zeros((rows, cols), complex)
for i in range(rows):
for j in range(cols):
out[var_name][i,j] = complex(real_imag[2*i+2*rows*j], real_imag[2*i+1+2*rows*j])
#arrays of scalars (implemented as list of scalars)
# --- bArray ---
elif 'bArray' == var_type:
size = unpack('Q', f.read(8))[0]
fmt = str(size)+'b'
out[var_name] = [uint8(n) for n in unpack(fmt, f.read(size))]
# --- sArray ---
elif 'sArray' == var_type:
size = unpack('Q', f.read(8))[0]
fmt = str(size)+'h'
out[var_name] = [int16(n) for n in unpack(fmt, f.read(size*2))]
# --- iArray ---
elif 'iArray' == var_type:
size = unpack('Q', f.read(8))[0]
fmt = str(size)+'i'
out[var_name] = [int32(n) for n in unpack(fmt, f.read(size*4))]
# --- fArray ---
elif 'fArray' == var_type:
size = unpack('Q', f.read(8))[0]
fmt = str(size)+'f'
out[var_name] = [float32(n) for n in unpack(fmt, f.read(size*4))]
# --- dArray ---
elif 'dArray' == var_type:
size = unpack('Q', f.read(8))[0]
fmt = str(size)+'d'
out[var_name] = [float64(n) for n in unpack(fmt, f.read(size*8))]
# --- fcArray ---
elif 'fcArray' == var_type:
size = unpack('Q', f.read(8))[0]
fmt = str(2*size)+'f'
real_imag = unpack(fmt, f.read(2*size*4))
out[var_name] = list()
for i in range(size):
out[var_name].append(complex64(complex(real_imag[2*i], real_imag[2*i+1])))
# --- dcArray ---
elif 'dcArray' == var_type:
size = unpack('Q', f.read(8))[0]
fmt = str(2*size)+'d'
real_imag = unpack(fmt, f.read(2*size*8))
out[var_name] = list()
for i in range(size):
out[var_name].append(complex128(complex(real_imag[2*i], real_imag[2*i+1])))
#arrays of vectors (implemented as list of vectors)
# --- bvecArray ---
elif 'bvecArray' == var_type:
size = unpack('Q', f.read(8))[0]
out[var_name] = list()
for i in range(size):
length = unpack('Q', f.read(8))[0]
fmt = str(length)+'b'
out[var_name].append(mat(unpack(fmt, f.read(length)), 'uint8').T)
# --- svecArray ---
elif 'svecArray' == var_type:
size = unpack('Q', f.read(8))[0]
out[var_name] = list()
for i in range(size):
length = unpack('Q', f.read(8))[0]
fmt = str(length)+'h'
out[var_name].append(mat(unpack(fmt, f.read(length*2)), 'int16').T)
# --- ivecArray ---
elif 'ivecArray' == var_type:
size = unpack('Q', f.read(8))[0]
out[var_name] = list()
for i in range(size):
length = unpack('Q', f.read(8))[0]
fmt = str(length)+'i'
out[var_name].append(mat(unpack(fmt, f.read(length*4)), 'int32').T)
# --- vecArray ---
elif 'vecArray' == var_type:
size = unpack('Q', f.read(8))[0]
out[var_name] = list()
for i in range(size):
length = unpack('Q', f.read(8))[0]
fmt = str(length)+'d'
out[var_name].append(mat(unpack(fmt, f.read(length*8)), 'float64').T)
# --- cvecArray ---
elif 'cvecArray' == var_type:
size = unpack('Q', f.read(8))[0]
out[var_name] = list()
for i in range(size):
length = unpack('Q', f.read(8))[0]
fmt = str(2*length)+'d'
real_imag = unpack(fmt, f.read(2*length*8))
v = zeros((length, 1), complex)
for j in range(length):
v[j,0] = complex128(complex(real_imag[2*j], real_imag[2*j+1]))
out[var_name].append(v)
# --- stringArray ---
elif 'stringArray' == var_type:
size = unpack('Q', f.read(8))[0]
out[var_name] = list()
for i in range(size):
length = unpack('Q', f.read(8))[0]
fmt = str(length)+'c'
out[var_name].append("".join(unpack(fmt, f.read(length))))
#arrays of matrices (implemented as list of matrices)
# --- bmatArray ---
elif 'bmatArray' == var_type:
size = unpack('Q', f.read(8))[0]
out[var_name] = list()
for i in range(size):
rows = unpack('Q', f.read(8))[0]
cols = unpack('Q', f.read(8))[0]
fmt = str(rows*cols)+'b'
out[var_name].append(reshape(mat(unpack(fmt, f.read(rows*cols)), 'uint8'), (cols, rows)).T)
# --- smatArray ---
elif 'smatArray' == var_type:
size = unpack('Q', f.read(8))[0]
out[var_name] = list()
for i in range(size):
rows = unpack('Q', f.read(8))[0]
cols = unpack('Q', f.read(8))[0]
fmt = str(rows*cols)+'h'
out[var_name].append(reshape(mat(unpack(fmt, f.read(rows*cols*2)), 'int16'), (cols, rows)).T)
# --- imatArray ---
elif 'imatArray' == var_type:
size = unpack('Q', f.read(8))[0]
out[var_name] = list()
for i in range(size):
rows = unpack('Q', f.read(8))[0]
cols = unpack('Q', f.read(8))[0]
fmt = str(rows*cols)+'i'
out[var_name].append(reshape(mat(unpack(fmt, f.read(rows*cols*4)), 'int32'), (cols, rows)).T)
# --- matArray ---
elif 'matArray' == var_type:
size = unpack('Q', f.read(8))[0]
out[var_name] = list()
for i in range(size):
rows = unpack('Q', f.read(8))[0]
cols = unpack('Q', f.read(8))[0]
fmt = str(rows*cols)+'d'
out[var_name].append(reshape(mat(unpack(fmt, f.read(rows*cols*8)), 'float64'), (cols, rows)).T)
# --- cmatArray ---
elif 'cmatArray' == var_type:
size = unpack('Q', f.read(8))[0]
out[var_name] = list()
for i in range(size):
rows = unpack('Q', f.read(8))[0]
cols = unpack('Q', f.read(8))[0]
fmt = str(2*rows*cols)+'d'
real_imag = unpack(fmt, f.read(2*rows*cols*8))
m = zeros((rows, cols), complex)
for j in range(rows):
for k in range(cols):
m[j,k] = complex128(complex(real_imag[2*j+2*rows*k], real_imag[2*j+1+2*rows*k]))
out[var_name].append(m)
else:
print 'Not a supported type: ', var_type
if pos+header_data_block_sizes[2] >= file_size:
break
else:
f.seek(pos+header_data_block_sizes[2], SEEK_SET)
f.close()
return out
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